Our aim is to advance genome analysis by providing interactive exploratory visualization tools enabling researchers to refine, correct and augment initial automated results. The cost of sequencing a genome has been dramatically reduced by several orders of magnitude in the last decade, and the natural consequence is that more and more researchers are sequencing more and more new genomes, both within populations and across every species. Each new exome or genome sequenced requires visualization because computational genome analysis remains an imperfect art, but with effective visualization, genome interpretation can take advantage of human perceptual capabilities to integrate information. Web Apollo will provide an easy to use, web-based environment offering multiple, distributed users high-performance visualization for interactive exploration of genome data, and the tools needed for producing highly accurate annotations that are maximally informative for downstream analysis. This project will enable every member of the burgeoning genomic community to fully mobilize molecular research data, whether collected by individual researchers or made available from public aggregation sites to benefit investigations into genetics and human diseases. This project will enable researchers to (i) Annotate genomic variants and haplotypes using: summarization tracks including background population frequencies and quality scores; projections of variants onto protein displays to see intersection with protein features such as active sites and secondary structure to assess biological impact; and individualized haplotype tracks to annotate unique disease associated transcripts; (ii) Improve exon-intron accuracy for protein coding transcripts via direct comparison to related proteins in an integrated multiple sequence alignment (MSA) view; (iii) Annotate biological function with GO terms (iv) Explore the genome using bookmarking of key sites for rapid navigation, and a novel cylindrical view to examine large-scale chromosomal rearrangements across multiple haplotypes simultaneously; (v) Analyze the data interactively using graph thresholding to define discrete features, dynamic visual filtering of data tracks and their contents, and visual folding of the genome to bring genes with shared attributes such as paralogy or shared phenotypes and the variants within them in the same visual field; (vi) Collaborate securely in real-time with selected team members over the Internet using hybrid clouds for private data and freely-hosted servers for public data.